Abstract
We present a new objective function for the registration of multi-modal medical images. Our novel similarity metric incorporates both knowledge about the current observations and information gained from previous registration results and combines the relative influence of these two types of information in a principled way. We show that in the absence of prior information, the method reduces approximately to the popular entropy minimization approach of registration and we provide a theoretical comparison of incorporating prior information in our and other currently existing methods. We also demonstrate experimental results on real images.
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Zöllei, L., Wells, W. (2006). Multi-modal Image Registration Using Dirichlet-Encoded Prior Information. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds) Biomedical Image Registration. WBIR 2006. Lecture Notes in Computer Science, vol 4057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11784012_5
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DOI: https://doi.org/10.1007/11784012_5
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